Deep learning-based extraction of Kenya's historical road network from topographic maps.

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Bibliographic Details
Title: Deep learning-based extraction of Kenya's historical road network from topographic maps.
Authors: Kramm T; GIS and Remote Sensing Group, Institute of Geography, University of Cologne, 50674, Cologne, Germany. tanja.kramm@uni-koeln.de., Nyamari N; Ecosystem Research Group, Institute of Geography, Faculty of Mathematics and Natural Sciences, University of Cologne, 50674, Cologne, Germany., Moseti V; Center for Development Research (ZEF), University of Bonn, 53113, Bonn, Germany., Klee A; GIS and Remote Sensing Group, Institute of Geography, University of Cologne, 50674, Cologne, Germany., Vehlken L; GIS and Remote Sensing Group, Institute of Geography, University of Cologne, 50674, Cologne, Germany., Anderson DM; Department of History, University of Warwick, Faculty of Arts Building, Coventry, CV4 7EQ, United Kingdom., Bogner C; Ecosystem Research Group, Institute of Geography, Faculty of Mathematics and Natural Sciences, University of Cologne, 50674, Cologne, Germany.; Global South Studies Center, University of Cologne, 50931, Cologne, Germany., Bareth G; GIS and Remote Sensing Group, Institute of Geography, University of Cologne, 50674, Cologne, Germany.
Source: Scientific data [Sci Data] 2025 Jul 05; Vol. 12 (1), pp. 1149. Date of Electronic Publication: 2025 Jul 05.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101640192 Publication Model: Electronic Cited Medium: Internet ISSN: 2052-4463 (Electronic) Linking ISSN: 20524463 NLM ISO Abbreviation: Sci Data Subsets: MEDLINE; PubMed not MEDLINE
Database: MEDLINE Ultimate
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ISSN:2052-4463
DOI:10.1038/s41597-025-05442-6